WhatsApp Group Integration
AI Agent Facilitating Group Planning With Friends
ROLE
Product Manager + Designer
Scope
Zero to One
Timeline
2 Months
A WhatsApp-based planning assistant designed to reduce decision fatigue in group chats. I led the product direction and MVP development through a pivot and two supporting MVPs. Each MVP offered unique insights that informed product strategy and shaped the path forward.
Project Scope
Over 10 weeks, I led the full product development cycle to address a familiar but underserved problem: making group plans actually happen in messaging apps like WhatsApp.
The project focused on building a conversational agent that could support the entire planning journey - from idea to execution - while working within real constraints of time and technical feasibility. The goal was to validate the concept through lean experimentation and define a solution that users could trust, understand, and actually use.
Frameworks Used
To bring Llama to life, I applied a variety of product management frameworks across the development journey. I started by identifying a clear customer pain point through interviews and mapped out the MVP using the MoSCoW method to prioritize features. I defined the value proposition and success metrics through OKRs and KPIs, tested key flows via user testing, and iterated based on real feedback. Finally, I wrapped the process with a go-to-market strategy informed by behavior insights and usage data.
testing hypothesis
Decisions will happen faster and with more confidence if people are presented with a visual summary comparing 2-3 curated options and a detailed breakdown, directly inside WhatsApp.
Rather than iterate linearly, I built two complementary MVPs in parallel to explore both functionality and user experience. One was a lightweight functional agent that mimicked interaction within WhatsApp; the other, a high-fidelity design prototype that visualized the complete planning flow.
Goal
Understand how users interact with a search agent embedded in WhatsApp and collect actual data on the type of queries.
Set Up
I created a business WhatsApp account and built an integration between WhatsApp and ChatGPT through Agentive, where I configured the agent's instruction base.
Limitations
✕ WhatsApp Business accounts can't be added to group chats, so testing was limited to 1:1 conversations.
✕ No visual UI, responses were purely text-based.
✕ Users couldn’t take action on core planning features like polls or availability pickers.
Testing Results
★ Top searches: restaurants, flights, coffee
★ 102 total messages exchanged
★ 2 to 5 messages per chat session
✕ 40% of users wanted increased speed
✕ 75% of users were not happy with the results
★ 100% of users said they would use it if results improved
This MVP didn’t solve the chaos in group planning but it gave users the feel of a search companion inside their most-used messaging app.
Goal
Explore how users experience a full planning flow within a group chat and test whether design-led interactions increase engagement and decision-making speed.
Limitations
✕ Built as a non-functional Figma prototype (not tested live in WhatsApp).
✕ Could not validate backend or technical feasibility.
✕ Users couldn’t interact with real data or trigger actual notifications.
User Stories
⇢As a group planner, I want all the links, ideas, and options to be organized so we don’t lose track of what’s been shared.
⇢ As someone coordinating with friends, I want to easily see everyone’s availability so we can pick a time that works.
⇢ As a group trying to finalize a plan, we want to create a poll with our final options.
⇢ As someone who wants the plan to happen, I want automatic reminders and quick-action options so that everyone stays on track and shows up.
Testing Results
★ 5/5 users were able to add their availability, though 2/5 wanted to edit the time.
★ 5/5 successfully responded to poll creation.
✕ 0/5 knew how to edit poll results.
✕ 75% of users were not happy with the results.
★ 100% of users said they would use it if results improved.
While it lacked functionality, the design validated that users valued visual clarity, quick decision tools, and having structure built into the chat itself.
Success for the agent is about follow-through: helping people go from ideas to action and making sure plans actually happen. These OKRs will guide the strategy: